Exploiting Machine Learning Models for Identification of Heart Diseases
Keywords:Heart Disease, Cardiovascular Diseases, Machine Learning, heart diseases Prediction
Cardiovascular illnesses have surpassed disease as the leading cause of death in industrialized, emerging, and underprivileged countries in recent decades. The mortality rate can be lowered through early detection and effective care of cardiac diseases. However, reliable detection of heart disorders in all conditions and serving doctors with 24-hour medical consultations are not possible since they require more intelligence, time, and talent to train the computer for a specific duty. Artificial intelligence and machine learning algorithms may have a substantial impact on the lives of those who are afflicted with chronic diseases. In this work, we used artificial intelligence to create a hybrid model for the prediction of cardiac disease and flags for prevention. The goal of this research was to develop a prediction model that can identify and characterize a patient's illness. We used data of 68975 patients from the University of California, Irvine's repository and trained several algorithms for CVD illness prediction. In our simulation maximum classifiers have achieved the greatest accuracy, which is a huge accomplishment for our research team. Our proposed hybrid ML algorithm framework improved CVD prediction performance when compared to previously offered prediction models. The proposed methods are decision tree and random forest models, which have a 99.98% accuracy for training, followed by support vector machine, which has a 99.30% accuracy followed by other classifiers which yield higher results in comparison. Pre-existing CVD history was considered the most important factor determining the prediction model's accuracy. Our findings reveal that our CVD prediction model based on machine learning techniques developed for health screening datasets is simple to apply and more accurate. Our proposed classifiers have the best accuracy rate.
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This is an open Access Article published by Research Center of Computing & Biomedical Informatics (RCBI), Lahore, Pakistan under CCBY 4.0 International License